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Comparison Study of Different Types of Setting Reference Movements for Mechatronic Objects by Parametric Splines

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Comparison Study of Different Types of Setting Reference Mov...

Ocean Studies (ICOS), International Conference on

作者： Anton Gubankov Igor Gornostaev Robotics Laboratory Institute of Automation and Control Processes FEB RAS Sevastopol State University Vladivostok Sevastopol Russia Laboratory of Robotic Systems Institute of Marine Technology Problems FEB RAS Vladivostok Russia

ISBN: (纸本)9781665473736

The paper presents a comparative study of various ways of specifying program movements for mechatronic objects (MO) along complex spatial trajectories defined by parametric Bezier splines, B-splines and third-order Catmull-Rom splines. An expression is obtained to describe the dependence of the rate of change of the parameter setting the desired position of the MO with the program speed of its movement, taking into account information about the type of trajectory. As a result, using this expression, which is universal for all the specified parametric splines of the third order, it is ensured that the MO moves along the trajectory exactly at a given speed while simultaneously changing its orientation as desired. The application of the methods considered in this paper does not require complex iterative calculations and allows one to perform all the necessary calculations in real time, even on low-power on-board computers of various robots and robotic complexes. Numerical modeling was carried out to analyze the features of the use of these splines in solving practical problems.

关键词： Mechatronics Oceans Numerical simulation Real-time systems Mathematical models Trajectory Numerical models

High SNR Processing for Low-Light Images 4

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High SNR Processing for Low-Light Images

2021 IEEE 4th International Conference on Big Data and Artificial Intelligence, BDAI 2021

作者： Cao, Wei Wu, Shi-Qian Wang, Rui-Ping Wu, Jia-Xin Deng, Gao-Xu Wuhan University of Science and Technology School of Information Science and Engineering Wuhan China Wuhan University of Science and Technology School of Machinery and Automation Wuhan China Wuhan University of Science and Technology Institute of Robotics and Intelligent Systems Wuhan China

ISBN: (纸本)9781665412704

This image enhancement on a single image for better visibility and higher SNR (signal-to-noise ratio) is an under-constrained problem, which becomes more challenging as this image is under the low-light condition. In order to solve the above problems, we firstly analyze the reason that classical Retinex enhancement result has low SNR due to ignoring the influence of noise in reflectance. To this end, combined with the actual illumination-reflectance prior, a new convex optimization function is proposed to estimate noise-suppressed/detail-preserved reflectance and spatial piece-wise smoothed illumination simultaneously. Especially, although the objective of the proposed method is a hybrid non-smooth problem, by subtly decomposition of the objective, we can solve it effectively. The final low-light enhancement result with pleasant visual performance and high SNR can be effectively obtained according to recompose the adjusting illumination and reflectance. Qualitative and quantitative experiments have illustrated that the proposed method has a higher SNR and more pleasing visual effect than state-of-the-art image enhanced techniques under low-light conditions. © 2021 IEEE.

关键词： Reflection

3D Face Reconstruction with the Geometric Guidance of Facial Part Segmentation

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arXiv 2023年

作者： Wang, Zidu Zhu, Xiangyu Zhang, Tianshuo Wang, Baiqin Lei, Zhen State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences China School of Artificial Intelligence University of Chinese Academy of Sciences China Centre for Artificial Intelligence and Robotics Hong Kong Institute of Science & Innovation Chinese Academy of Sciences Hong Kong

3D Morphable Models (3DMMs) provide promising 3D face reconstructions in various applications. However, existing methods struggle to reconstruct faces with extreme expressions due to deficiencies in supervisory signals, such as sparse or inaccurate landmarks. Segmentation information contains effective geometric contexts for face reconstruction. Certain attempts intuitively depend on differentiable renderers to compare the rendered silhouettes of reconstruction with segmentation, which is prone to issues like local optima and gradient instability. In this paper, we fully utilize the facial part segmentation geometry by introducing Part Re-projection Distance Loss (PRDL). Specifically, PRDL transforms facial part segmentation into 2D points and re-projects the reconstruction onto the image plane. Subsequently, by introducing grid anchors and computing different statistical distances from these anchors to the point sets, PRDL establishes geometry descriptors to optimize the distribution of the point sets for face reconstruction. PRDL exhibits a clear gradient compared to the renderer-based methods and presents state-of-the-art reconstruction performance in extensive quantitative and qualitative experiments. Our project is available at https://***/wang-zidu/3DDFA-V3. © 2023, CC BY-NC-ND.

关键词： Geometry

Graphics Capsule: Learning Hierarchical 3D Face Representations from 2D Images

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arXiv 2023年

作者： Yu, Chang Zhu, Xiangyu Zhang, Xiaomei Zhang, Zhaoxiang Lei, Zhen State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences China School of Artificial Intelligence University of Chinese Academy of Sciences China Centre for Artificial Intelligence and Robotics Hong Kong Institute of Science & Innovation Chinese Academy of Sciences Hong Kong

The function of constructing the hierarchy of objects is important to the visual process of the human brain. Previous studies have successfully adopted capsule networks to decompose the digits and faces into parts in an unsupervised manner to investigate the similar perception mechanism of neural networks. However, their descriptions are restricted to the 2D space, limiting their capacities to imitate the intrinsic 3D perception ability of humans. In this paper, we propose an Inverse Graphics Capsule Network (IGC-Net) to learn the hierarchical 3D face representations from large-scale unlabeled images. The core of IGC-Net is a new type of capsule, named graphics capsule, which represents 3D primitives with interpretable parameters in computer graphics (CG), including depth, albedo, and 3D pose. Specifically, IGC-Net first decomposes the objects into a set of semantic-consistent part-level descriptions and then assembles them into object-level descriptions to build the hierarchy. The learned graphics capsules reveal how the neural networks, oriented at visual perception, understand faces as a hierarchy of 3D models. Besides, the discovered parts can be deployed to the unsupervised face segmentation task to evaluate the semantic consistency of our method. Moreover, the part-level descriptions with explicit physical meanings provide insight into the face analysis that originally runs in a black box, such as the importance of shape and texture for face recognition. Experiments on CelebA, BP4D, and Multi-PIE demonstrate the characteristics of our IGC-Net. Copyright © 2023, The Authors. All rights reserved.

关键词： Face recognition

Design of a Novel Autonomous Docking System With a Supporting Robotic Fish

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Design of a Novel Autonomous Docking System With a Supportin...

2023 IEEE International Conference on Real-Time Computing and robotics, RCAR 2023

作者： Dong, Huijie Zhao, Chunyang Qiao, Tiezhu Wang, Jian Chen, Di Meng, Yan Yu, Junzhi Taiyuan University of Technology Key Laboratory of Advanced Transducers and Intelligent Control System Ministry of Education Taiyuan030024 China Chinese Academy of Sciences Laboratory of Cognitive and Decision Intelligence for Complex System Institute of Automation Beijing100190 China Peking University State Key Laboratory for Turbulence and Complex Systems Department of Advanced Manufacturing and Robotics College of Engineering Beijing100871 China Guangdong Ocean University College of Electronics and Information Engineering Zhanjiang524088 China

ISBN: (纸本)9798350327182

Underwater supporting robots serving as a relay of energy supplements and communication for other underwater equipment are promising for ocean exploration, development, and protection. This paper proposes a novel autonomous docking system centered on a designed supporting robotic fish named 'CourierFish'. Specifically, CourierFish is capable of docking with a surface dock station for supplying itself and docking with a seafloor platform for supporting equipment in the platform. A visual navigation scheme integrating LED and ArUco markers is presented for accurate localization. The control approach for docking motion is also illustrated. Simulations and aquatic experiments are performed to verify the feasibility of the proposed docking system. The obtained results lay a solid foundation for the construction of various underwater equipment and robot networks. © 2023 IEEE.

关键词： Fish

Branch-Tuning: Balancing Stability and Plasticity for Continual Self-Supervised Learning

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arXiv 2024年

作者： Liu, Wenzhuo Zhu, Fei Liu, Cheng-Lin The University of Chinese Academy of Sciences Beijing China The State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences 95 Zhongguancun East Road Beijing100190 China The Centre for Artificial Intelligence and Robotics Hong Kong Institute of Science and Innovation Chinese Academy of Sciences Hong Kong999077 Hong Kong

Self-supervised learning (SSL) has emerged as an effective paradigm for deriving general representations from vast amounts of unlabeled data. However, as real-world applications continually integrate new content, the high computational and resource demands of SSL necessitate continual learning rather than complete retraining. This poses a challenge in striking a balance between stability and plasticity when adapting to new information. In this paper, we employ Centered Kernel Alignment for quantitatively analyzing model stability and plasticity, revealing the critical roles of batch normalization layers for stability and convolutional layers for plasticity. Motivated by this, we propose Branch-tuning, an efficient and straightforward method that achieves a balance between stability and plasticity in continual SSL. Branch-tuning consists of branch expansion and compression, and can be easily applied to various SSL methods without the need of modifying the original methods, retaining old data or models. We validate our method through incremental experiments on various benchmark datasets, demonstrating its effectiveness and practical value in real-world scenarios. We hope our work offers new insights for future continual self-supervised learning research. The code will be made publicly available. Copyright © 2024, The Authors. All rights reserved.

关键词： Supervised learning

3D Ultrasound Image Acquisition and Diagnostic Analysis of the Common Carotid Artery with a Portable Robotic Device

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3D Ultrasound Image Acquisition and Diagnostic Analysis of t...

IEEE/RSJ International Conference on Intelligent Robots and systems (IROS)

作者： Longyue Tan Zhaokun Deng Mingrui Hao Pengcheng Zhang Xilong Hou Chen Chen Xiaolin Gu Xiao-Hu Zhou Zeng-Guang Hou Shuangyi Wang State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences Beijing China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing China Center for Artificial Intelligence and Robotics Hong Kong Institute of Science and Innovation Chinese Academy of Sciences Hong Kong Lingshu Medical Company Suzhou China

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

Ultrasound (US) imaging of the carotid artery (CA) is a non-invasive diagnostic tool widely used in the medical field to assess the condition of the carotid artery, thereby predicting the risk of cardiovascular and cerebrovascular diseases. However, implementing this method in primary healthcare can be challenging due to the requirement for professionally trained sonographers. With the adoption of US robotic devices, the probe pose can be acquired while scanning, offering the possibility for 3D reconstruction and providing analyses that are not dependent on operator experience. This article introduces a method to semi-automatically acquire serialized US images of the common carotid artery (CCA). The method involves a specially designed robotic device built with a 6-RSU parallel mechanism, which is controlled according to robot pose, force sensor data and synchronous US images. To validate the images acquired, a method is proposed to segment the intima-media of CCA and calculate the intima-media thickness (IMT), which is a key indicator for cerebrovascular events prediction. After that, we propose an algorithm to reconstruct CCA into 3D voxel data with patient movement and cardiac cycle compensated, and a longitudinal view US image of CCA can be resliced from the voxel. The methods are tested on human subjects and the results indicate that the system and workflow can provide both quantitative and qualitative information of CCA for further diagnosis.

关键词： Image segmentation Three-dimensional displays Ultrasonic imaging Medical services Robot sensing systems Prediction algorithms Probes Carotid arteries Medical diagnostic imaging Intelligent robots

Design and Experiments of a Portable Robotic Ultrasound Scanning and Diagnosis System of Carotid Artery

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Design and Experiments of a Portable Robotic Ultrasound Scan...

International Conference on Advanced robotics and Mechatronics (ICARM)

作者： Longyue Tan Zhaokun Deng Mingrui Hao Pengcheng Zhang Xilong Hou Xiaolin Gu Chen Chen Shuangyi Wang State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences Beijing China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing China Center for Artificial Intelligence and Robotics Hong Kong Institute of Science and Innovation Chinese Academy of Sciences Hong Kong Lingshu Medical Company Suzhou China

ISBN: (数字)9798350385724

ISBN: (纸本)9798350385731

Ultrasound (US) imaging is an important, non-invasive method for evaluating the carotid artery (CA), which plays a critical role in early screening cardiovascular and cerebrovascular conditions. However, it requires specialized training for sonographers, which is a challenge in primary healthcare. Robotic devices now enable semi-automatic or fully automatic US scans, reducing the need for skilled operators. This paper presents an innovative, economically viable robotic system specifically engineered for conducting US examinations of the human CA. This system is distinguished by its advanced human-machine interaction capabilities with artificial intelligence assistance, which facilitate semi-automatic scanning processes in primary healthcare settings. Building upon this, the study introduces a semi-automatic scanning approach designed to control the device’s operations, aiming at capturing highquality images of the key structures of CA. Employing the images obtained through this method, the research proposes a segmentation strategy, leveraging fine-tuning adjustments on the LiteMedSAM model to achieve semantic segmentation of the CA in the images. The outcome is a nuanced diagnostic tool capable of identifying and evaluating carotid stenosis. The mentioned device and methods are tested on human subjects and the results indicate success on detecting critical vascular structures and positive feedback on human-machine interaction from operators and patients.

关键词： Human computer interaction Training Ultrasonic imaging Mechatronics Semantic segmentation Buildings Imaging Medical services Robots Carotid arteries

Defying Imbalanced Forgetting in Class Incremental Learning

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arXiv 2024年

作者： Xu, Shixiong Meng, Gaofeng Nie, Xing Ni, Bolin Fan, Bin Xiang, Shiming State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences China School of Artificial Intelligence University of Chinese Academy of Sciences China Centre for Artificial Intelligence and Robotics HK Institute of Science & Innovation Chinese Academy of Sciences China School of Intelligence Science and Technology University of Science and Technology Beijing China

We observe a high level of imbalance in the accuracy of different classes in the same old task for the first time. This intriguing phenomenon, discovered in replay-based Class Incremental Learning (CIL), highlights the imbalanced forgetting of learned classes, as their accuracy is similar before the occurrence of catastrophic forgetting. This discovery remains previously unidentified due to the reliance on average incremental accuracy as the measurement for CIL, which assumes that the accuracy of classes within the same task is similar. However, this assumption is invalid in the face of catastrophic forgetting. Further empirical studies indicate that this imbalanced forgetting is caused by conflicts in representation between semantically similar old and new classes. These conflicts are rooted in the data imbalance present in replay-based CIL methods. Building on these insights, we propose CLass-Aware Disentanglement (CLAD) to predict the old classes that are more likely to be forgotten and enhance their accuracy. Importantly, CLAD can be seamlessly integrated into existing CIL methods. Extensive experiments demonstrate that CLAD consistently improves current replay-based methods, resulting in performance gains of up to 2.56%. Copyright © 2024, The Authors. All rights reserved.

关键词： Machine learning